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Reassessing the Resource Curse using Causal Machine Learning

Author

Listed:
  • Roland Hodler

    (SoDa Laboratories, Monash University)

  • Michael Lechner

    (SoDa Laboratories, Monash University)

  • Paul A. Raschky

    (SoDa Laboratories, Monash University)

Abstract

We reassess the effects of natural resources on economic development and conflict, applying a causal forest estimator and data from 3,800 Sub-Saharan African districts. We find that, on average, mining activities and higher world market prices of locally mined minerals both increase economic development and conflict. Consistent with the previous literature, mining activities have more positive effects on economic development and weaker effects on conflict in places with low ethnic diversity and high institutional quality. In contrast, the effects of changes in mineral prices vary little in ethnic diversity and institutional quality, but are non-linear and largest at relatively high prices.

Suggested Citation

  • Roland Hodler & Michael Lechner & Paul A. Raschky, 2020. "Reassessing the Resource Curse using Causal Machine Learning," SoDa Laboratories Working Paper Series 2020-01, Monash University, SoDa Laboratories.
    • Handle: RePEc:ajr:sodwps:2020-01
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    References listed on IDEAS

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    1. Nils B. Weidmann & Doreen Kuse & Kristian Skrede Gleditsch, 2010. "The Geography of the International System: The CShapes Dataset," International Interactions, Taylor & Francis Journals, vol. 36(1), pages 86-106, February.
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    Cited by:

    1. Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series 2104, University of St. Gallen, School of Economics and Political Science.
    2. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.

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    More about this item

    Keywords

    resource curse; economic development; conflict; causal machine learning; Africa;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O55 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Africa
    • Q34 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Natural Resources and Domestic and International Conflicts
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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